1. Field of the Invention
The present invention relates to an image information coding apparatus for compressing and coding halftone image information.
2. Related Background Art
According to a typical conventional image transmission and storage technique, information is coded to improve transmission and storage efficiency so as to suppress redundancy (generally called compression).
Along with recent developments of digital image processing techniques and device techniques, an image subjected to transmission and storage is converted from binary data to multivalue data. The type of image also tends to be changed from a monochromatic image to a full-color image. In addition, the resolution of the image to be transmitted and stored has been increasing. As a result, the number of data to be transmitted and stored is increased, and highly efficient coding techniques are required.
However, most of the conventional coding schemes are proposed for binary images, as represented by MH, MR, and MMR schemes used for facsimile communication.
These coding schemes are not essentially suitable for coding of multivalue images. Several coding schemes for multivalue images are available, such as block coding, predictive coding, and orthogonal transformation coding. However, these coding schemes are used especially for television images and are not suitable for coding of general documents, photographs, and graphic images. In particular, in orthogonal transformation coding, pixel blocks of an image are simply orthogonally transformed to obtain quantized scalar values. Therefore, redundancy of the image cannot be satisfactorily suppressed.
In order to further improve coding efficiency, vector quantization is proposed as a coding scheme which is close to maximum coding efficiency in principle. A so-called code book is required in vector quantization. The number of input patterns to be vector-quantized is dramatically increased. It is difficult to perform vector quantization by using a conventional code book generation method.
Vector quantization poses the following problems.
The first problem is a way of calculating optimum reproduction vectors of an image. In conventional vector quantization, various images having different frequencies are used to perform a training sequence, thereby calculating reproduction vectors. Reproduction vectors having minimum distortion are found for an input image, thereby coding the input image. A vector quantization algorithm called an LBG scheme is generally used to obtain optimum solutions of the reproduction vectors. However, when vector quantization of various image data having different frequencies are performed by the LBG method, reproduction vectors for image data which frequently appear in the training sequence are obtained as the optimum solutions of vector quantization. Therefore, when an image is vector-quantized by this method, a characteristic image portion having a low generation frequency is typically degraded, resulting in poor appearance.
The second problem is bulky hardware when vector quantization is realized by hardware. That is, in order to perform vector quantization in units of blocks, a large look-up table (LUT) for receiving all image data within each block and outputting codes of corresponding reproduction vectors is required. For this reason, a block size for vector quantization is limited. When the block size is small, a correlation between the images cannot be conveniently utilized, and therefore a compression ratio is undesirably kept low.